Quality improvement collaborative for improving patient care delivery in Argentine public health sector intensive care units.

BACKGROUND: The demand for healthcare services during the COVID-19 pandemic was excessive for less-resourced settings, with intensive care units (ICUs) taking the heaviest toll. OBJECTIVE: The aim was to achieve adequate personal protective equipment (PPE) use in 90% of patient encounters, to reach 90% compliance with objectives of patient flow (OPF) and to provide emotional support tools to 90% of healthcare workers (HCWs). METHODS: We conducted a quasi-experimental study with an interrupted time-series design in 14 ICUs in Argentina. We randomly selected adult critically ill patients admitted from July 2020 to July 2021 and active HCWs in the same period. We implemented a quality improvement collaborative (QIC) with a baseline phase (BP) and an intervention phase (IP). The QIC included learning sessions, periods of action and improvement cycles (plan-do-study-act) virtually coached by experts via platform web-based activities. The main study outcomes encompassed the following elements: proper utilisation of PPE, compliance with nine specific OPF using daily goal sheets through direct observations and utilisation of a web-based tool for tracking emotional well-being among HCWs. RESULTS: We collected 7341 observations of PPE use (977 in BP and 6364 in IP) with an improvement in adequate use from 58.4% to 71.9% (RR 1.2, 95% CI 1.17 to 1.29, p<0.001). We observed 7428 patient encounters to evaluate compliance with 9 OPF (879 in BP and 6549 in IP) with an improvement in compliance from 53.9% to 67% (RR 1.24, 95% CI 1.17 to 1.32, p<0.001). The results showed that HCWs did not use the support tool for self-mental health evaluation as much as expected. CONCLUSION: A QIC was effective in improving healthcare processes and adequate PPE use, even in the context of a pandemic, indicating the possibility of expanding QIC networks nationwide to improve overall healthcare delivery. The limited reception of emotional support tools requires further analyses.

Transparency interventions to improve health system outcomes in low and middle-income countries: a narrative systematic review.

OBJECTIVES: This study aimed to identify the types of transparency interventions in the health systems of the low-income and middle-income countries and the outcomes of such interventions in those systems. METHOD: We searched major medical databases including PubMed, Embase and Scopus, for any kind of interventional study on transparency in health systems. We also looked for additional sources of information in organisational websites, grey literature and reference checking. Using the PRISMA algorithm for identifying related studies, we included 24 articles. RESULTS: Our initial search, from 1980 to August 2021, retrieved 407 articles, 24 of which were narratively analysed. Response to a problem (mostly corruption) was the main reason for the initiation of a transparency intervention. Transparency interventions differed in terms of types, performance methods, collaboration partners and outcomes. They help improve the health system mostly in the short term and in some cases, long term. CONCLUSION: Although our findings revealed that transparency initiatives could reduce some problems such as counterfeit drugs and corruption, and improve health indicators in a short term, still their sustainability remains a concern. Health systems need robust interventions with clearly defined and measured outcomes, especially sustainable outcomes to tackle corruption fundamentally.

Opportunities and challenges on hospital preparedness to handle motorcycle accidents in Busia County, Kenya: an exploratory qualitative study.

Introduction: motorcycles continue to be a popular mode of transport in Kenya. However, the related injuries cause significant morbidity and mortality and remain to be a major and neglected public health issue. This raised the crucial need for hospital preparedness in managing morbidities and in reducing mortalities. This formed the basis of this paper which aims to document the challenges and opportunities in the healthcare system in handling motorcycle accidents in a Kenyan border town in Busia County. Methods: we drew data from an exploratory qualitative study that was carried out in 2021. All six referral hospitals purposively included in the study. The study targeted a total of 25 top level facility managers as key informants on the facility level opportunities and challenges in handling motorcycle accidents. Descriptive data were analyzed using SPSS version 20. Results: the hospitals were not well prepared to handle motorcycle accidents. The major challenges were understaffing in critical care services; inadequate/lack of equipment to handle motorcycle injuries; inadequate/lack of infrastructure i.e. surgical wards, emergency rooms, inadequate space, functional theatre; lack/inadequate supplies; overstretched referral services arising from the hinge burden of motorcycle accidents in the area; inadequate specialized personnel to provide trauma/care services; mishandling of cases at the site of accident; inability of victims to pay related bills; inappropriate identification of victims at the facility; lack/inadequate on-job training. Some opportunities that currently exist include health system interventions which are not limited to employment of more professionals, improvement of infrastructure, provision of equipment and increase of budgetary allocation. Conclusion: the study reveals vast challenges that are faced by hospitals in managing patients. This calls for the government to step in and capitalize on the proposed opportunities by the health managers to be able to manage morbidities and bring down mortalities due to motorcycle accidents.

What is the effectiveness of reporting systems in promoting learning in healthcare?

Patient safety in healthcare remains a top priority. Learning from safety events is vital to move towards safer systems. As a result, reporting systems are recognised as the cornerstone of safety, especially in high-risk industries. However, in healthcare, the benefits of reporting systems in promoting learning remain contentious. Though the strengths of these systems, such as promoting a safety culture and providing information from near misses are noted, there are problems that mean learning is missed. Understanding the factors that both enable and act as barriers to learning from reporting is also important to consider. This review, considers the effectiveness of reporting systems in contributing to learning in healthcare.

Exploring the impact of compassion and leadership on patient safety and quality in healthcare systems: a narrative review.

BACKGROUND: Patient safety and healthcare quality are considered integral parts of the healthcare system that are driven by a dynamic combination of human and non-human factors. This review article provides an insight into the two major human factors that impact patient safety and quality including compassion and leadership. It also discusses how compassion is different from empathy and explores the impact of both compassion and leadership on patient safety and healthcare quality. In addition, this review also provides strategies for the improvement of patient safety and healthcare quality through compassion and effective leadership. METHODS: This narrative review explores the existing literature on compassion and leadership and their combined impact on patient safety and healthcare quality. The literature for this purpose was gathered from published research articles, reports, recommendations and guidelines. RESULTS: The findings from the literature suggest that both compassion and transformational leadership can create a positive culture where healthcare professionals (HCPs) prioritise patient safety and quality. Leaders who exhibit compassion are more likely to inspire their teams to deliver patient-centred care and focus on error prevention. CONCLUSION: Compassion can become an antidote for the burnout of HCPs. Compassion is a behaviour that is not only inherited but can also be learnt. Both compassionate care and transformational leadership improve organisational culture, patient experience, patient engagement, outcomes and overall healthcare excellence. We propose that transformational leadership that reinforces compassion remarkably improves patient safety, patient engagement and quality.

Women's trust in the healthcare system in Serbia: Validation of the Women's Trust and Confidence in Healthcare System scale.

BACKGROUND: Women's role as patients is associated with power relationships embedded in society. Although trust in the health care system is a general prerequisite for positive health outcomes, practices regarding women's agency in healthcare systems in Southeastern Europe reinforce women's passivity. Most of the current psychological measures of trust have been constructed and validated in "WEIRD" (samples that are drawn from populations that are White, Educated, Industrialized, Rich, and Democratic) countries, thus having a limited application in other social contexts. OBJECTIVES: We aimed to construct an instrument for assessing women's trust in healthcare systems to describe the structure of trust: Women's Trust and Confidence in the Healthcare System scale. DESIGN: Two independent samples (N1 = 329; N2 = 333) of adult women in Serbia voluntarily completed an online questionnaire. The questionnaire comprised 20 trust-related items which were selected from an extensive collection of women's experiences in the healthcare system and evaluated by experts on a 5-point Likert-type scale. METHODS: We used exploratory factor analysis of the Women's Trust and Confidence in the Healthcare System scale to analyze the structure of trust in the first sample data set and validated it with the second sample using confirmatory factor analysis. We tested concurrent validity by exploring how women's trust in the healthcare system predicts health-related behaviors (multigroup structural equation modeling). All analyses were conducted using R statistical software. RESULTS: The Women's Trust and Confidence in the Healthcare System scale (Cronbach's alpha = 0.86) indicated a three-factor structure of trust in the healthcare system: trust in healthcare professionals, distrust in the public healthcare system, and confidence in healthcare system. This was validated using an independent sample. Interpersonal trust positively predicted women's desirable health behaviors, while trust in the system had a negative impact. CONCLUSION: The Women's Trust and Confidence in the Healthcare System scale captures women's trust in a paternalistic healthcare system, is reliable, and has a stable three-factor structure. The study's findings reveal the relationship between women's trust and health-related behavior: in paternalistic environments, trust reinforces women's passivity.

Experiences of UK clinical scientists (Physical Sciences modality) with their regulator, the Health and Care Professions Council: results of a 2022 survey.

BACKGROUND: In healthcare, regulation of professions is an important tool to protect the public. With increasing regulation however, professions find themselves under increasing scrutiny. Recently there has also been considerable concern with regulator performance, with high profile reports pointing to cases of inefficiency and bias. Whilst reports have often focused on large staff groups, such as doctors, in the literature there is a dearth of data on the experiences of smaller professional groups such Clinical Scientists with their regulator, the Health and Care Professions Council. This article reports the findings of a survey from Clinical Scientists (Physical Sciences modality) about their experiences with their regulator, and their perception of the quality and safety of that regulation. METHODS: Between July-October 2022, a survey was conducted via the Medical Physics and Engineering mail-base, open to all medical physicists & engineers. Questions covered typical topics of registration, communication, audit and fitness to practice. The questionnaire consisted of open and closed questions. Likert scoring, and thematic analysis were used to assess the quantitative and qualitative data. RESULTS: Of 146 responses recorded, analysis was based on 143 respondents. Overall survey sentiment was significantly more negative than positive, in terms of regulator performance (negative responses 159; positive 106; significant at p < 0.001). Continuous Professional Development audit was rated median 4; other topics were rated as neutral (fitness to practice, policies & procedures); and some as poor (value). CONCLUSIONS: The Clinical Scientist (Physical Sciences) professional registrants rated the performance of their regulator more negatively than other reported assessments (by the Professional Standards Authority). Survey respondents suggested a variety of performance aspects, such as communication and fitness to practice, would benefit from improvement. Indications from this small dataset, suggest a larger survey of HCPC registrants would be useful.

Preliminary adaptation of the systems thinking for everyday work cue card set in a US healthcare system: a pragmatic and participatory co-design approach.

INTRODUCTION: Healthcare is a highly complex adaptive system, requiring a systems approach to understand its behaviour better. We adapt the Systems Thinking for Everyday Work (STEW) cue cards, initially introduced as a systems approach tool in the UK, in a US healthcare system as part of a study investigating the feasibility of a systems thinking approach for front-line workers. METHODS: The original STEW cards were adapted using consensus-building methods with front-line staff and safety leaders. RESULTS: Each card was examined for relevance, applicability, language and aesthetics (colour, style, visual cues and size). Two sets of cards were created due to the recognition that systems thinking was relatively new in healthcare and that the successful use of the principles on the cards would need initial facilitation to ensure their effective application. Six principles were agreed on and are presented in the cards: Your System outlines the need to agree that problems belong to a system and that the system must be defined. Viewpoints ensure that multiple voices are heard within the discussion. Work Condition highlights the resources, constraints and barriers that exist in the system and contribute to the system's functions. Interactions ask participants to understand how parts of the system interact to perform the work. Performance guides users to understand how work can be performed daily. Finally, Understanding seeks to promote a just cultural environment of appreciating that people do what makes sense to them. The two final sets of cards were scored using a content validity survey, with a final score of 1. CONCLUSIONS: The cards provide an easy-to-use guide to help users understand the system being studied, learn from problems encountered and understand the everyday work involved in providing excellent care. The cards offer a practical 'systems approach' for use within complex healthcare systems.

The need for cybersecurity self-evaluation in healthcare.

The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur.

Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS).

CONTEXT: The COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust. METHODS: We conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively). FINDINGS: We present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations. CONCLUSIONS: This global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.

AI Quality Standards in Health Care: Rapid Umbrella Review.

BACKGROUND: In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE: This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS: We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS: We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS: Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

The management of rare disease patients from a grassroot perspective: the role of patients' organizations in the global recognition of rare diseases in Cameroon.

Introduction: rare diseases (RD) are extremely complex health conditions. Persons affected by these conditions in Cameroon are often neglected in society and health systems through the inexistence of policies and programs. In Cameroon, there exists no program or policy conceived to address their needs in terms of access to quality health care, timely and reliable diagnosis, treatments, education, etc. The consequence is that persons living with a RD (PLWRD) and their families do not participate in social life. The unique fate of PLWRD reveals that the principle of social justice and equity is flawed in Cameroon. However, patients, in order to survive in society, rely on patients' organizations (PO) to improve their quality of life (QoL) and advocate for a better consideration in the society. The aim of this paper is to highlight how initiatives from a grassroot perspective like POs can inform decision-makers to address the needs of PLWRD and their families. Methods: the study associated a systematic literature review and semi-structured interviews with parents of children suffering from a RD and who are members of a PO. Through the systematic literature review we highlighted the impact POs have in the development of research on RDs, patient literacy, patient empowerment and advocacy while semi-structured interviews brought out the needs of patients and their families. Results: findings, on the one hand show that, in Cameroon PLWRD face a number of challenges like the incurability of their condition, catastrophic medical expenses, stigmatization and marginalization, etc. and though in POs their QoL still remains poor. On the other hand, where POs are empowered they are key actors in research on RDs and help decision-makers on having a better insight into the type of RD that exists across a geographical area, the sociodemographic profile of patients, etc. for a better management of PLWRD. Conclusion: the study suggests that the ministry of public health should create a network with existing RD POs to adequately meet the needs of PLWRD.

The Risks and Challenges of Artificial Intelligence in Endocrinology.

Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.

Patient journey mapping: emerging methods for understanding and improving patient experiences of health systems and services.

Patient journey mapping is an emerging field of research that uses various methods to map and report evidence relating to patient experiences and interactions with healthcare providers, services, and systems. This research often involves the development of visual, narrative, and descriptive maps or tables, which describe patient journeys and transitions into, through, and out of health services. This methods corner paper presents an overview of how patient journey mapping has been conducted within the health sector, providing cardiovascular examples. It introduces six key steps for conducting patient journey mapping and describes the opportunities and benefits of using patient journey mapping and future implications of using this approach.

Health-care organization characteristics in cancer care delivery: an integrated conceptual framework with content validation.

Context can influence cancer-related outcomes. For example, health-care organization characteristics, including ownership, leadership, and culture, can affect care access, communication, and patient outcomes. Health-care organization characteristics and other contextual factors can also influence whether and how clinical discoveries reduce cancer incidence, morbidity, and mortality. Importantly, policy, market, and technology changes are transforming health-care organization design, culture, and operations across the cancer continuum. Consequently, research is essential to examine when, for whom, and how organizational characteristics influence person-level, organization-level, and population-level cancer outcomes. Understanding organizational characteristics-the structures, processes, and other features of entities involved in health care delivery-and their dynamics is an important yet understudied area of care delivery research across the cancer continuum. Research incorporating organizational characteristics is critical to address health inequities, test care delivery models, adapt interventions, and strengthen implementation. The field lacks conceptual grounding, however, to help researchers identify germane organizational characteristics. We propose a framework identifying organizational characteristics relevant for cancer care delivery research based on conceptual work in health services, organizational behavior, and management science and refined using a systematic review and key informant input. The proposed framework is a tool for organizing existing research and enhancing future cancer care delivery research. Following a 2012 Journal of the National Cancer Institute monograph, this work complements National Cancer Institute efforts to stimulate research addressing the relationship between cancer outcomes and contextual factors at the patient, provider, team, delivery organization, community, and health policy levels.

The Delivery Model of Perceived Medical Service Quality Based on Donabedian's Framework.

ABSTRACT: The implementation of the National Health Insurance has transformed the medical care landscape in Taiwan, rendering perceived medical service quality (PMSQ) and patient satisfaction significant focal points in medical care management. Past studies mostly focused on the technical aspects of medical care services, while overlooking the patients' perception of services and the delivery process of PMSQ in the medical care experience. This study integrated the theoretical framework of the Donabedian SPO model and the SERVQUAL questionnaire. The survey was conducted among the outpatients of three types of medical institutions in northern Taiwan: academic medical centers, metropolitan hospitals, and local community hospitals. A total of 400 questionnaires were collected, and 315 valid questionnaires remained after eliminating the incomplete ones. This study established a PMSQ delivery model to explore patients' perceptions of medical service quality. It was found that the variable, Assurance, could deliver the PMSQ and enhance the Medical outcome (MO), while improving the variable, Tangible, in medical institutions could not significantly enhance the MO. These findings emphasize the importance of healthcare institutions prioritizing the professional background, demeanor of their healthcare staff, treatment methods, and processes over tangible elements.

How to Navigate the Pitfalls of AI Hype in Health Care.

In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues.